Mostly updated file paths
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@ -4,18 +4,18 @@
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# (2) Clustering
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# (3) Fit tree
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#
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# input: results/haum/dataframes_case_2019.RData
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# output: results/haum/eventlogs_2019_case-clusters.csv
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# results/haum/tmp_user-navigation.RData
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# input: results/dataframes_case_2019.RData
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# output: results/eventlogs_2019_case-clusters.csv
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# results/user-navigation.RData
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# ../../thesis/figures/data/clustering_cases.RData
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#
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# last mod: 2024-03-15
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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#--------------- (1) Load data ---------------
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load("results/haum/dataframes_case_2019.RData")
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load("results/dataframes_case_2019.RData")
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#--------------- (2) Clustering ---------------
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@ -119,13 +119,13 @@ aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, m
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aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, median)
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write.table(res,
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file = "results/haum/eventlogs_2019_case-clusters.csv",
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file = "results/eventlogs_2019_case-clusters.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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save(res, dist_mat, hcs, acs, coor_2d, coor_3d,
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file = "results/haum/tmp_user-navigation.RData")
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file = "results/user-navigation.RData")
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save(coor_2d, coor_3d, cluster, dattree,
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file = "../../thesis/figures/data/clustering_cases.RData")
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@ -6,10 +6,10 @@
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# (4) Clustering
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# (5) Fit tree
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# output: results/haum/eventlogs_pre-corona_case-clusters.csv
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# output: --
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#
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# last mod: 2024-03-15
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -17,7 +17,7 @@ source("R_helpers.R")
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#--------------- (1) Read data ---------------
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load("results/haum/eventlogs_pre-corona_cleaned.RData")
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load("results/eventlogs_pre-corona_cleaned.RData")
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# Select one year to handle number of cases
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dat <- dat[as.Date(dat$date.start) > "2017-12-31" &
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@ -77,8 +77,8 @@ dattree18 <- data.frame(case = datcase18$case,
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)
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# centrality <- pbapply::pbsapply(dattree18$case, get_centrality, data = dat)
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# save(centrality, file = "results/haum/tmp_centrality_2018.RData")
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load("results/haum/tmp_centrality_2018.RData")
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# save(centrality, file = "results/centrality_2018.RData")
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load("results/centrality_2018.RData")
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dattree18$BetweenCentrality <- centrality
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@ -3,10 +3,11 @@
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# content: (1) Read data
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# (2) Investigate variants
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#
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# input: results/haum/eventlogs_pre-corona_case-clusters.RData
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# output:
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# input: results/eventlogs_pre-corona_cleaned.RData
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# output: ../../thesis/figures/freq-traces.pdf
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# ../../thesis/figures/freq-traces_powerlaw.pdf
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#
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# last mod: 2024-03-12
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -14,7 +15,7 @@ library(bupaverse)
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#--------------- (1) Read data ---------------
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load("results/haum/eventlogs_pre-corona_cleaned.RData")
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load("results/eventlogs_pre-corona_cleaned.RData")
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#--------------- (2) Investigate variants ---------------
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@ -27,7 +28,7 @@ alog <- activitylog(dat,
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resource_id = "path",
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timestamps = c("start", "complete"))
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pdf("results/figures/freq-traces.pdf", height = 7, width = 6, pointsize = 10)
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pdf("../../thesis/figures/freq-traces.pdf", height = 7, width = 6, pointsize = 10)
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trace_explorer(alog, n_traces = 25)
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# --> sequences of artworks are just too rare
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dev.off()
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@ -54,7 +55,7 @@ y <- as.numeric(tab)
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p1 <- lm(log(y) ~ log(x))
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pre <- exp(coef(p1)[1]) * x^coef(p1)[2]
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pdf("results/figures/freq-traces_powerlaw.pdf", height = 3.375,
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pdf("../../thesis/figures/freq-traces_powerlaw.pdf", height = 3.375,
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width = 3.375, pointsize = 10)
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par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
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@ -1,25 +1,28 @@
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# 13_dfgs-case-clusters.R
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#
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# content:
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# content: (1) Read data
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# (2) Export DFGs for clusters
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#
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# input: results/haum/tmp_user-navigation.RData
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# output:
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# input: results/user-navigation.RData
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# output: ../../thesis/figures/dfg_cases_cluster1_R.pdf
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# ../../thesis/figures/dfg_cases_cluster2_R.pdf
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# ../../thesis/figures/dfg_cases_cluster3_R.pdf
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# ../../thesis/figures/dfg_cases_cluster4_R.pdf
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# ../../thesis/figures/dfg_cases_cluster5_R.pdf
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#
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# last mod: 2024-03-19
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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load("results/haum/tmp_user-navigation.RData")
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#--------------- (1) Read data ---------------
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#dat <- read.table("results/haum/eventlogs_2019_case-clusters.csv", header = TRUE, sep = ";")
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load("results/user-navigation.RData")
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dat <- res
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dat$start <- as.POSIXct(dat$date.start)
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dat$complete <- as.POSIXct(dat$date.stop)
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alog <- bupaR::activitylog(dat[dat$cluster == cluster, ],
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case_id = "case",
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activity_id = "item",
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@ -33,13 +36,9 @@ tab <- table(tr$absolute_frequency)
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tab[1] / nrow(tr)
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alog |> edeaR::filter_infrequent_flows(min_n = 20) |> processmapR::process_map()
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## Export DFGs for clusters
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#--------------- (2) Export DFGs for clusters ---------------
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mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")
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cl_names <- c("Scanning", "Exploring", "Flitting", "Searching", "Info")
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@ -63,12 +62,11 @@ for (i in 1:5) {
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render = FALSE)
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processmapR::export_map(dfg,
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file_name = paste0("results/processmaps/dfg_cases_cluster", i, "_R.pdf"),
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file_name = paste0("../../thesis/figures/dfg_cases_cluster", i, "_R.pdf"),
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file_type = "pdf",
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title = cl_names[i])
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}
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# cluster 1: 50
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# cluster 2: 30 o. 20
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# cluster 3: 20 - 30
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@ -87,7 +85,6 @@ get_percent_variants <- function(log, cluster, min_n) {
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nrow(alog)
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}
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perc <- numeric(5)
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for (i in 1:5) {
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